Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.
Logistic Regression (LR)
Random Forest (RF)
eXtreme Gradient Boosting (XGBoost)
flood sensitivity assessment
Journal
Water science and technology : a journal of the International Association on Water Pollution Research
ISSN: 0273-1223
Titre abrégé: Water Sci Technol
Pays: England
ID NLM: 9879497
Informations de publication
Date de publication:
May 2024
May 2024
Historique:
received:
24
11
2023
accepted:
24
04
2024
medline:
1
6
2024
pubmed:
1
6
2024
entrez:
1
6
2024
Statut:
ppublish
Résumé
Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.
Identifiants
pubmed: 38822603
pii: wst_2024_146
doi: 10.2166/wst.2024.146
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2605-2624Subventions
Organisme : the National Key R&D Program of China
ID : 2022YFC3800500
Informations de copyright
© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
Déclaration de conflit d'intérêts
The authors declare there is no conflict.
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